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A newartificial tidings ( AI)tool can engender effigy in under two seconds — and it does n’t need expensive computer hardware to run .
South Korean scientists have used a special technique call in knowledge distillation to compress the size of an unresolved source ( or publicly usable ) picture propagation role model eff as Stable Diffusion XL — which has 2.56 billion parameter , or variables the AI apply to learn during training .
The tool can run on low-cost graphics processing units (GPUs) and needs roughly 8GB of RAM to process requests
The little version of the young modeling , known as " KOALA " , has just 700 million parameters , mean it ’s lean enough to run quickly and without needing expensive and energy - intensive computer hardware .
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The method they used , noesis distillate , transfers cognition from a bombastic model to a smaller one , ideally without compromising performance . The welfare of a little model is that it take less fourth dimension to perform computation and bring forth an reply .
The tool can function on low - cost graphics processing units ( GPUs ) and involve roughly 8 Gi of RAM to summons request — versus larger models , which need gamey - terminal industrial GPUs .
The team publish their finding in a paper Dec. 7 , 2023 to the preprint databasearXiv . They have also made their work uncommitted via the open author AI repositoryHugging Face .
The Electronics and Telecommunication Research Institute ( ETRI ) , the innovation behind the unexampled modeling , has created five versions include three version of the " KOALA " image generator — which generates images base on text input signal — and two versions of " Ko - LLaVA " — which can reply text - based questions with look-alike or television .
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When they test KOALA , it father an ikon based on the prompt " a picture of an astronaut read a book under the moon on Mars " in 1.6 seconds . OpenAI ’s DALL·E 2 render an epitome based on the same prompt in 12.3 seconds , and DALL·E 3 generated it in 13.7 seconds , according to astatement .
The scientist now plan to integrate the technology they ’ve develop into subsist image generation services , education services , content production and other lines of business .
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